Overview

Dataset statistics

Number of variables18
Number of observations42
Missing cells88
Missing cells (%)11.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.0 KiB
Average record size in memory147.0 B

Variable types

Text1
Unsupported1
Numeric15
Categorical1

Alerts

Co2-Emissions per ton is highly overall correlated with GDP and 1 other fieldsHigh correlation
GDP is highly overall correlated with Co2-Emissions per ton and 1 other fieldsHigh correlation
Population is highly overall correlated with Co2-Emissions per ton and 1 other fieldsHigh correlation
Infant mortality is highly overall correlated with GDP per capita and 2 other fieldsHigh correlation
Minimum wage is highly overall correlated with Unemployment rate and 2 other fieldsHigh correlation
Unemployment rate is highly overall correlated with Minimum wage and 2 other fieldsHigh correlation
Population: Labor force participation (%) is highly overall correlated with Minimum wage and 2 other fieldsHigh correlation
temperature is highly overall correlated with ideal temperature?High correlation
GDP per capita is highly overall correlated with Infant mortality and 4 other fieldsHigh correlation
Human Development Index (2021) is highly overall correlated with Infant mortality and 2 other fieldsHigh correlation
Prevalence of moderate or severe food insecurity in the total population (percent) (2022) is highly overall correlated with Infant mortality and 3 other fieldsHigh correlation
ideal temperature? is highly overall correlated with temperatureHigh correlation
Country Code has 42 (100.0%) missing valuesMissing
Agricultural Land( %) has 2 (4.8%) missing valuesMissing
Co2-Emissions per ton has 1 (2.4%) missing valuesMissing
CPI has 1 (2.4%) missing valuesMissing
Infant mortality has 1 (2.4%) missing valuesMissing
Minimum wage has 10 (23.8%) missing valuesMissing
Unemployment rate has 2 (4.8%) missing valuesMissing
Population: Labor force participation (%) has 2 (4.8%) missing valuesMissing
temperature has 13 (31.0%) missing valuesMissing
ideal temperature? has 12 (28.6%) missing valuesMissing
Gini's index has 2 (4.8%) missing valuesMissing
Country has unique valuesUnique
GDP has unique valuesUnique
Population has unique valuesUnique
GDP per capita has unique valuesUnique
Country Code is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-11-17 04:23:26.879545
Analysis finished2023-11-17 04:23:45.926454
Duration19.05 seconds
Software versionydata-profiling vv4.6.1
Download configurationconfig.json

Variables

Country
Text

UNIQUE 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size464.0 B
2023-11-17T12:23:46.155708image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Length

Max length32
Median length13
Mean length9.4285714
Min length4

Characters and Unicode

Total characters396
Distinct characters43
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique42 ?
Unique (%)100.0%

Sample

1st rowAlgeria
2nd rowAngola
3rd rowBenin
4th rowBotswana
5th rowBurkina Faso
ValueCountFrequency (%)
republic 3
 
5.0%
the 3
 
5.0%
of 2
 
3.3%
sudan 2
 
3.3%
congo 2
 
3.3%
south 2
 
3.3%
botswana 1
 
1.7%
burkina 1
 
1.7%
faso 1
 
1.7%
cape 1
 
1.7%
Other values (42) 42
70.0%
2023-11-17T12:23:46.499267image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 51
 
12.9%
i 39
 
9.8%
e 31
 
7.8%
o 27
 
6.8%
n 25
 
6.3%
18
 
4.5%
r 17
 
4.3%
u 17
 
4.3%
t 14
 
3.5%
b 12
 
3.0%
Other values (33) 145
36.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 321
81.1%
Uppercase Letter 56
 
14.1%
Space Separator 18
 
4.5%
Dash Punctuation 1
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 51
15.9%
i 39
12.1%
e 31
9.7%
o 27
 
8.4%
n 25
 
7.8%
r 17
 
5.3%
u 17
 
5.3%
t 14
 
4.4%
b 12
 
3.7%
s 11
 
3.4%
Other values (14) 77
24.0%
Uppercase Letter
ValueCountFrequency (%)
S 8
14.3%
C 6
10.7%
M 5
8.9%
T 5
8.9%
L 4
 
7.1%
G 4
 
7.1%
B 4
 
7.1%
A 4
 
7.1%
R 3
 
5.4%
E 3
 
5.4%
Other values (7) 10
17.9%
Space Separator
ValueCountFrequency (%)
18
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 377
95.2%
Common 19
 
4.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 51
 
13.5%
i 39
 
10.3%
e 31
 
8.2%
o 27
 
7.2%
n 25
 
6.6%
r 17
 
4.5%
u 17
 
4.5%
t 14
 
3.7%
b 12
 
3.2%
s 11
 
2.9%
Other values (31) 133
35.3%
Common
ValueCountFrequency (%)
18
94.7%
- 1
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 396
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 51
 
12.9%
i 39
 
9.8%
e 31
 
7.8%
o 27
 
6.8%
n 25
 
6.3%
18
 
4.5%
r 17
 
4.3%
u 17
 
4.3%
t 14
 
3.5%
b 12
 
3.0%
Other values (33) 145
36.6%

Country Code
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing42
Missing (%)100.0%
Memory size464.0 B

Agricultural Land( %)
Real number (ℝ)

MISSING 

Distinct40
Distinct (%)100.0%
Missing2
Missing (%)4.8%
Infinite0
Infinite (%)0.0%
Mean0.443375
Minimum0.034
Maximum0.798
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size464.0 B
2023-11-17T12:23:46.601266image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.034
5-th percentile0.0798
Q10.305
median0.452
Q30.61925
95-th percentile0.7361
Maximum0.798
Range0.764
Interquartile range (IQR)0.31425

Descriptive statistics

Standard deviation0.21835876
Coefficient of variation (CV)0.49249227
Kurtosis-0.88676363
Mean0.443375
Median Absolute Deviation (MAD)0.1635
Skewness-0.24832681
Sum17.735
Variance0.047680548
MonotonicityNot monotonic
2023-11-17T12:23:46.688333image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0.174 1
 
2.4%
0.461 1
 
2.4%
0.614 1
 
2.4%
0.385 1
 
2.4%
0.424 1
 
2.4%
0.635 1
 
2.4%
0.471 1
 
2.4%
0.361 1
 
2.4%
0.507 1
 
2.4%
0.034 1
 
2.4%
Other values (30) 30
71.4%
(Missing) 2
 
4.8%
ValueCountFrequency (%)
0.034 1
2.4%
0.038 1
2.4%
0.082 1
2.4%
0.087 1
2.4%
0.116 1
2.4%
0.174 1
2.4%
0.196 1
2.4%
0.206 1
2.4%
0.28 1
2.4%
0.287 1
2.4%
ValueCountFrequency (%)
0.798 1
2.4%
0.776 1
2.4%
0.734 1
2.4%
0.719 1
2.4%
0.715 1
2.4%
0.712 1
2.4%
0.702 1
2.4%
0.69 1
2.4%
0.648 1
2.4%
0.635 1
2.4%

Co2-Emissions per ton
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct41
Distinct (%)100.0%
Missing1
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean28020.902
Minimum121
Maximum476644
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size464.0 B
2023-11-17T12:23:46.779051image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum121
5-th percentile293
Q11386
median3905
Q310983
95-th percentile150006
Maximum476644
Range476523
Interquartile range (IQR)9597

Descriptive statistics

Standard deviation83777.232
Coefficient of variation (CV)2.9898121
Kurtosis21.944087
Mean28020.902
Median Absolute Deviation (MAD)3300
Skewness4.5125849
Sum1148857
Variance7.0186246 × 109
MonotonicityNot monotonic
2023-11-17T12:23:46.867286image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
150006 1
 
2.4%
605 1
 
2.4%
1298 1
 
2.4%
2739 1
 
2.4%
4349 1
 
2.4%
7943 1
 
2.4%
4228 1
 
2.4%
2017 1
 
2.4%
121 1
 
2.4%
10902 1
 
2.4%
Other values (31) 31
73.8%
ValueCountFrequency (%)
121 1
2.4%
202 1
2.4%
293 1
2.4%
297 1
2.4%
532 1
2.4%
543 1
2.4%
605 1
2.4%
620 1
2.4%
1093 1
2.4%
1298 1
2.4%
ValueCountFrequency (%)
476644 1
2.4%
238560 1
2.4%
150006 1
2.4%
50564 1
2.4%
34693 1
2.4%
29937 1
2.4%
20000 1
2.4%
16670 1
2.4%
14870 1
2.4%
11973 1
2.4%

CPI
Real number (ℝ)

MISSING 

Distinct41
Distinct (%)100.0%
Missing1
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean303.45512
Minimum103.62
Maximum4583.71
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size464.0 B
2023-11-17T12:23:46.953823image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum103.62
5-th percentile106.58
Q1120.25
median155.33
Q3187.43
95-th percentile418.34
Maximum4583.71
Range4480.09
Interquartile range (IQR)67.18

Descriptive statistics

Standard deviation712.25598
Coefficient of variation (CV)2.3471542
Kurtosis34.780779
Mean303.45512
Median Absolute Deviation (MAD)35.08
Skewness5.7750475
Sum12441.66
Variance507308.58
MonotonicityNot monotonic
2023-11-17T12:23:47.034983image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
151.36 1
 
2.4%
129.96 1
 
2.4%
418.34 1
 
2.4%
135.02 1
 
2.4%
129.91 1
 
2.4%
182.31 1
 
2.4%
157.97 1
 
2.4%
109.32 1
 
2.4%
185.09 1
 
2.4%
109.25 1
 
2.4%
Other values (31) 31
73.8%
ValueCountFrequency (%)
103.62 1
2.4%
105.51 1
2.4%
106.58 1
2.4%
109.25 1
2.4%
109.32 1
2.4%
110.5 1
2.4%
110.71 1
2.4%
111.65 1
2.4%
113.3 1
2.4%
118.65 1
2.4%
ValueCountFrequency (%)
4583.71 1
2.4%
1344.19 1
2.4%
418.34 1
2.4%
288.57 1
2.4%
268.36 1
2.4%
262.95 1
2.4%
261.73 1
2.4%
234.16 1
2.4%
223.13 1
2.4%
212.31 1
2.4%

GDP
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9169697 × 1010
Minimum4.290166 × 108
Maximum3.5143165 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size464.0 B
2023-11-17T12:23:47.119515image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum4.290166 × 108
5-th percentile1.3583121 × 109
Q13.8288468 × 109
median1.4132175 × 1010
Q33.7667158 × 1010
95-th percentile1.6629421 × 1011
Maximum3.5143165 × 1011
Range3.5100263 × 1011
Interquartile range (IQR)3.3838311 × 1010

Descriptive statistics

Standard deviation7.3250921 × 1010
Coefficient of variation (CV)1.8700916
Kurtosis11.194184
Mean3.9169697 × 1010
Median Absolute Deviation (MAD)1.0937558 × 1010
Skewness3.2952655
Sum1.6451273 × 1012
Variance5.3656974 × 1021
MonotonicityNot monotonic
2023-11-17T12:23:47.206445image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
1.699882364 × 10111
 
2.4%
1698843063 1
 
2.4%
7666704427 1
 
2.4%
7593752450 1
 
2.4%
1.418044456 × 10101
 
2.4%
1.493415993 × 10101
 
2.4%
1.236652772 × 10101
 
2.4%
1.292814512 × 10101
 
2.4%
429016605 1
 
2.4%
2.357808405 × 10101
 
2.4%
Other values (32) 32
76.2%
ValueCountFrequency (%)
429016605 1
2.4%
1185728677 1
2.4%
1340389411 1
2.4%
1698843063 1
2.4%
1763819048 1
2.4%
1981845741 1
2.4%
2220307369 1
2.4%
2460072444 1
2.4%
3070518100 1
2.4%
3318716359 1
2.4%
ValueCountFrequency (%)
3.514316492 × 10111
2.4%
3.031751276 × 10111
2.4%
1.699882364 × 10111
2.4%
9.61076624 × 10101
2.4%
9.463541587 × 10101
2.4%
6.698363422 × 10101
2.4%
6.317706818 × 10101
2.4%
5.207625095 × 10101
2.4%
4.73196242 × 10101
2.4%
3.879770992 × 10101
2.4%

Population
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21634658
Minimum97625
Maximum1.1207873 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size464.0 B
2023-11-17T12:23:47.301466image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum97625
5-th percentile501990.9
Q12384412
median11747935
Q329516854
95-th percentile85378952
Maximum1.1207873 × 108
Range1.119811 × 108
Interquartile range (IQR)27132442

Descriptive statistics

Standard deviation27138514
Coefficient of variation (CV)1.2543999
Kurtosis3.5468401
Mean21634658
Median Absolute Deviation (MAD)10154618
Skewness1.9280443
Sum9.0865565 × 108
Variance7.3649892 × 1014
MonotonicityNot monotonic
2023-11-17T12:23:47.386463image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
43053054 1
 
2.4%
97625 1
 
2.4%
18628747 1
 
2.4%
4525696 1
 
2.4%
1265711 1
 
2.4%
30366036 1
 
2.4%
2494530 1
 
2.4%
23310715 1
 
2.4%
215056 1
 
2.4%
16296364 1
 
2.4%
Other values (32) 32
76.2%
ValueCountFrequency (%)
97625 1
2.4%
215056 1
2.4%
483628 1
2.4%
850886 1
2.4%
973560 1
2.4%
1093238 1
2.4%
1265711 1
2.4%
1920922 1
2.4%
2125268 1
2.4%
2346179 1
2.4%
ValueCountFrequency (%)
112078730 1
2.4%
100388073 1
2.4%
86790567 1
2.4%
58558270 1
2.4%
58005463 1
2.4%
44269594 1
2.4%
43053054 1
2.4%
42813238 1
2.4%
31825295 1
2.4%
30792608 1
2.4%

Infant mortality
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct40
Distinct (%)97.6%
Missing1
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean41.62439
Minimum10.2
Maximum84.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size464.0 B
2023-11-17T12:23:47.473417image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum10.2
5-th percentile13.6
Q130
median39.1
Q351.6
95-th percentile68.2
Maximum84.5
Range74.3
Interquartile range (IQR)21.6

Descriptive statistics

Standard deviation18.039371
Coefficient of variation (CV)0.43338462
Kurtosis-0.28253343
Mean41.62439
Median Absolute Deviation (MAD)12.2
Skewness0.23423945
Sum1706.6
Variance325.41889
MonotonicityNot monotonic
2023-11-17T12:23:47.556463image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
54 2
 
4.8%
20.1 1
 
2.4%
12.4 1
 
2.4%
35.3 1
 
2.4%
51.5 1
 
2.4%
13.6 1
 
2.4%
29 1
 
2.4%
48 1
 
2.4%
24.4 1
 
2.4%
31.8 1
 
2.4%
Other values (30) 30
71.4%
ValueCountFrequency (%)
10.2 1
2.4%
12.4 1
2.4%
13.6 1
2.4%
14.6 1
2.4%
16.7 1
2.4%
18.1 1
2.4%
20.1 1
2.4%
24.4 1
2.4%
28.5 1
2.4%
29 1
2.4%
ValueCountFrequency (%)
84.5 1
2.4%
78.5 1
2.4%
68.2 1
2.4%
65.7 1
2.4%
64.9 1
2.4%
63.7 1
2.4%
60.5 1
2.4%
54 2
4.8%
53.5 1
2.4%
51.6 1
2.4%

Minimum wage
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct27
Distinct (%)84.4%
Missing10
Missing (%)23.8%
Infinite0
Infinite (%)0.0%
Mean0.4721875
Minimum0.01
Maximum2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size464.0 B
2023-11-17T12:23:47.642596image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.1065
Q10.2325
median0.345
Q30.54
95-th percentile1.3685
Maximum2
Range1.99
Interquartile range (IQR)0.3075

Descriptive statistics

Standard deviation0.44487573
Coefficient of variation (CV)0.94215906
Kurtosis6.2833161
Mean0.4721875
Median Absolute Deviation (MAD)0.15
Skewness2.4136806
Sum15.11
Variance0.19791442
MonotonicityNot monotonic
2023-11-17T12:23:47.719769image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.41 2
 
4.8%
0.29 2
 
4.8%
0.34 2
 
4.8%
0.27 2
 
4.8%
0.71 2
 
4.8%
0.21 1
 
2.4%
0.01 1
 
2.4%
0.47 1
 
2.4%
0.09 1
 
2.4%
0.57 1
 
2.4%
Other values (17) 17
40.5%
(Missing) 10
23.8%
ValueCountFrequency (%)
0.01 1
2.4%
0.09 1
2.4%
0.12 1
2.4%
0.13 1
2.4%
0.16 1
2.4%
0.17 1
2.4%
0.18 1
2.4%
0.21 1
2.4%
0.24 1
2.4%
0.27 2
4.8%
ValueCountFrequency (%)
2 1
2.4%
1.88 1
2.4%
0.95 1
2.4%
0.88 1
2.4%
0.71 2
4.8%
0.68 1
2.4%
0.57 1
2.4%
0.53 1
2.4%
0.47 1
2.4%
0.41 2
4.8%

Unemployment rate
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct40
Distinct (%)100.0%
Missing2
Missing (%)4.8%
Infinite0
Infinite (%)0.0%
Mean0.0844825
Minimum0.0047
Maximum0.2818
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size464.0 B
2023-11-17T12:23:47.807214image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.0047
5-th percentile0.01836
Q10.03345
median0.0643
Q30.11835
95-th percentile0.20427
Maximum0.2818
Range0.2771
Interquartile range (IQR)0.0849

Descriptive statistics

Standard deviation0.066654316
Coefficient of variation (CV)0.78897187
Kurtosis0.8445553
Mean0.0844825
Median Absolute Deviation (MAD)0.0408
Skewness1.1459795
Sum3.3793
Variance0.0044427979
MonotonicityNot monotonic
2023-11-17T12:23:47.887264image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0.117 1
 
2.4%
0.066 1
 
2.4%
0.0565 1
 
2.4%
0.0955 1
 
2.4%
0.0667 1
 
2.4%
0.0324 1
 
2.4%
0.2027 1
 
2.4%
0.0047 1
 
2.4%
0.1337 1
 
2.4%
0.0443 1
 
2.4%
Other values (30) 30
71.4%
(Missing) 2
 
4.8%
ValueCountFrequency (%)
0.0047 1
2.4%
0.0176 1
2.4%
0.0184 1
2.4%
0.0198 1
2.4%
0.0204 1
2.4%
0.0208 1
2.4%
0.0223 1
2.4%
0.0247 1
2.4%
0.0281 1
2.4%
0.0324 1
2.4%
ValueCountFrequency (%)
0.2818 1
2.4%
0.2341 1
2.4%
0.2027 1
2.4%
0.1856 1
2.4%
0.1819 1
2.4%
0.1653 1
2.4%
0.1602 1
2.4%
0.1337 1
2.4%
0.1225 1
2.4%
0.1224 1
2.4%

Population: Labor force participation (%)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct38
Distinct (%)95.0%
Missing2
Missing (%)4.8%
Infinite0
Infinite (%)0.0%
Mean0.650575
Minimum0.412
Maximum0.861
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size464.0 B
2023-11-17T12:23:47.973808image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.412
5-th percentile0.4558
Q10.57875
median0.6785
Q30.74975
95-th percentile0.83115
Maximum0.861
Range0.449
Interquartile range (IQR)0.171

Descriptive statistics

Standard deviation0.12363965
Coefficient of variation (CV)0.19004673
Kurtosis-0.90074004
Mean0.650575
Median Absolute Deviation (MAD)0.0865
Skewness-0.34695756
Sum26.023
Variance0.015286763
MonotonicityNot monotonic
2023-11-17T12:23:48.244615image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0.72 3
 
7.1%
0.412 1
 
2.4%
0.579 1
 
2.4%
0.459 1
 
2.4%
0.583 1
 
2.4%
0.781 1
 
2.4%
0.595 1
 
2.4%
0.578 1
 
2.4%
0.457 1
 
2.4%
0.56 1
 
2.4%
Other values (28) 28
66.7%
(Missing) 2
 
4.8%
ValueCountFrequency (%)
0.412 1
2.4%
0.433 1
2.4%
0.457 1
2.4%
0.459 1
2.4%
0.461 1
2.4%
0.464 1
2.4%
0.484 1
2.4%
0.497 1
2.4%
0.56 1
2.4%
0.578 1
2.4%
ValueCountFrequency (%)
0.861 1
2.4%
0.834 1
2.4%
0.831 1
2.4%
0.796 1
2.4%
0.781 1
2.4%
0.776 1
2.4%
0.775 1
2.4%
0.767 1
2.4%
0.763 1
2.4%
0.761 1
2.4%

temperature
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct29
Distinct (%)100.0%
Missing13
Missing (%)31.0%
Infinite0
Infinite (%)0.0%
Mean26.441379
Minimum16.42
Maximum31.42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size464.0 B
2023-11-17T12:23:48.321128image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum16.42
5-th percentile20.404
Q125.22
median26.45
Q329.42
95-th percentile31.208
Maximum31.42
Range15
Interquartile range (IQR)4.2

Descriptive statistics

Standard deviation3.478271
Coefficient of variation (CV)0.13154651
Kurtosis1.3590889
Mean26.441379
Median Absolute Deviation (MAD)1.45
Skewness-0.82060137
Sum766.8
Variance12.098369
MonotonicityNot monotonic
2023-11-17T12:23:48.395606image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
26.3 1
 
2.4%
25 1
 
2.4%
26.75 1
 
2.4%
26.52 1
 
2.4%
21.37 1
 
2.4%
29.48 1
 
2.4%
23.49 1
 
2.4%
31.36 1
 
2.4%
19.76 1
 
2.4%
27.19 1
 
2.4%
Other values (19) 19
45.2%
(Missing) 13
31.0%
ValueCountFrequency (%)
16.42 1
2.4%
19.76 1
2.4%
21.37 1
2.4%
23.49 1
2.4%
23.5 1
2.4%
24.66 1
2.4%
25 1
2.4%
25.22 1
2.4%
25.35 1
2.4%
25.37 1
2.4%
ValueCountFrequency (%)
31.42 1
2.4%
31.36 1
2.4%
30.98 1
2.4%
30.92 1
2.4%
30.78 1
2.4%
29.71 1
2.4%
29.48 1
2.4%
29.42 1
2.4%
27.83 1
2.4%
27.19 1
2.4%

ideal temperature?
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)6.7%
Missing12
Missing (%)28.6%
Memory size464.0 B
1.0
22 
0.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters90
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 22
52.4%
0.0 8
 
19.0%
(Missing) 12
28.6%

Length

2023-11-17T12:23:48.474156image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-17T12:23:48.548057image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 22
73.3%
0.0 8
 
26.7%

Most occurring characters

ValueCountFrequency (%)
0 38
42.2%
. 30
33.3%
1 22
24.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 60
66.7%
Other Punctuation 30
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 38
63.3%
1 22
36.7%
Other Punctuation
ValueCountFrequency (%)
. 30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 90
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 38
42.2%
. 30
33.3%
1 22
24.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 38
42.2%
. 30
33.3%
1 22
24.4%
Distinct40
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1021.6929
Minimum18.1
Maximum3200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size464.0 B
2023-11-17T12:23:48.616093image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum18.1
5-th percentile89.15
Q1435.75
median955
Q31470.5
95-th percentile2387.95
Maximum3200
Range3181.9
Interquartile range (IQR)1034.75

Descriptive statistics

Standard deviation740.14613
Coefficient of variation (CV)0.72443115
Kurtosis0.71373148
Mean1021.6929
Median Absolute Deviation (MAD)548.5
Skewness0.86272206
Sum42911.1
Variance547816.3
MonotonicityNot monotonic
2023-11-17T12:23:48.700095image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
900 2
 
4.8%
788 2
 
4.8%
89 1
 
2.4%
2330 1
 
2.4%
92 1
 
2.4%
2041 1
 
2.4%
1032 1
 
2.4%
285 1
 
2.4%
151 1
 
2.4%
3200 1
 
2.4%
Other values (30) 30
71.4%
ValueCountFrequency (%)
18.1 1
2.4%
56 1
2.4%
89 1
2.4%
92 1
2.4%
151 1
2.4%
207 1
2.4%
220 1
2.4%
228 1
2.4%
250 1
2.4%
285 1
2.4%
ValueCountFrequency (%)
3200 1
2.4%
2526 1
2.4%
2391 1
2.4%
2330 1
2.4%
2041 1
2.4%
1651 1
2.4%
1646 1
2.4%
1604 1
2.4%
1577 1
2.4%
1543 1
2.4%

Gini's index
Real number (ℝ)

MISSING 

Distinct39
Distinct (%)97.5%
Missing2
Missing (%)4.8%
Infinite0
Infinite (%)0.0%
Mean42.2425
Minimum27.6
Maximum63
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size464.0 B
2023-11-17T12:23:48.787502image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum27.6
5-th percentile31.975
Q136.525
median42.25
Q345.625
95-th percentile56.06
Maximum63
Range35.4
Interquartile range (IQR)9.1

Descriptive statistics

Standard deviation8.0398969
Coefficient of variation (CV)0.1903272
Kurtosis0.13903558
Mean42.2425
Median Absolute Deviation (MAD)5.2
Skewness0.59145102
Sum1689.7
Variance64.639942
MonotonicityNot monotonic
2023-11-17T12:23:48.868491image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
43 2
 
4.8%
27.6 1
 
2.4%
36.8 1
 
2.4%
50.5 1
 
2.4%
59.1 1
 
2.4%
37.3 1
 
2.4%
40.7 1
 
2.4%
38.3 1
 
2.4%
32.1 1
 
2.4%
35.7 1
 
2.4%
Other values (29) 29
69.0%
(Missing) 2
 
4.8%
ValueCountFrequency (%)
27.6 1
2.4%
29.6 1
2.4%
32.1 1
2.4%
32.6 1
2.4%
32.8 1
2.4%
34.2 1
2.4%
34.8 1
2.4%
35 1
2.4%
35.3 1
2.4%
35.7 1
2.4%
ValueCountFrequency (%)
63 1
2.4%
59.1 1
2.4%
55.9 1
2.4%
54.6 1
2.4%
53.3 1
2.4%
51.3 1
2.4%
50.5 1
2.4%
50.3 1
2.4%
48.9 1
2.4%
46.6 1
2.4%

GDP per capita
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2594.3281
Minimum411.55234
Maximum17401.722
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size464.0 B
2023-11-17T12:23:48.955854image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum411.55234
5-th percentile469.1023
Q1711.16197
median1342.4329
Q33243.1636
95-th percentile7810.5115
Maximum17401.722
Range16990.169
Interquartile range (IQR)2532.0016

Descriptive statistics

Standard deviation3309.8885
Coefficient of variation (CV)1.2758173
Kurtosis9.8089339
Mean2594.3281
Median Absolute Deviation (MAD)792.52427
Skewness2.8776158
Sum108961.78
Variance10955362
MonotonicityNot monotonic
2023-11-17T12:23:49.040393image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
3948.343279 1
 
2.4%
17401.72152 1
 
2.4%
411.5523404 1
 
2.4%
1677.919253 1
 
2.4%
11203.54058 1
 
2.4%
491.8047231 1
 
2.4%
4957.458006 1
 
2.4%
554.6009687 1
 
2.4%
1994.906466 1
 
2.4%
1446.830965 1
 
2.4%
Other values (32) 32
76.2%
ValueCountFrequency (%)
411.5523404 1
2.4%
441.5056034 1
2.4%
467.9074407 1
2.4%
491.8047231 1
2.4%
504.4625434 1
2.4%
522.2198092 1
2.4%
545.2162123 1
2.4%
554.6009687 1
2.4%
621.8929536 1
2.4%
675.5422134 1
2.4%
ValueCountFrequency (%)
17401.72152 1
2.4%
11203.54058 1
2.4%
7817.183083 1
2.4%
7683.750611 1
2.4%
6001.400814 1
2.4%
4957.458006 1
2.4%
4097.87221 1
2.4%
3948.343279 1
2.4%
3467.958805 1
2.4%
3408.846254 1
2.4%

Human Development Index (2021)
Real number (ℝ)

HIGH CORRELATION 

Distinct39
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5622619
Minimum0.385
Maximum0.802
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size464.0 B
2023-11-17T12:23:49.130524image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.385
5-th percentile0.4061
Q10.48675
median0.532
Q30.61725
95-th percentile0.7443
Maximum0.802
Range0.417
Interquartile range (IQR)0.1305

Descriptive statistics

Standard deviation0.10664531
Coefficient of variation (CV)0.18967195
Kurtosis-0.37237658
Mean0.5622619
Median Absolute Deviation (MAD)0.0545
Skewness0.62532182
Sum23.615
Variance0.011373222
MonotonicityNot monotonic
2023-11-17T12:23:49.210540image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0.525 2
 
4.8%
0.479 2
 
4.8%
0.731 2
 
4.8%
0.745 1
 
2.4%
0.785 1
 
2.4%
0.556 1
 
2.4%
0.802 1
 
2.4%
0.446 1
 
2.4%
0.615 1
 
2.4%
0.4 1
 
2.4%
Other values (29) 29
69.0%
ValueCountFrequency (%)
0.385 1
2.4%
0.4 1
2.4%
0.404 1
2.4%
0.446 1
2.4%
0.449 1
2.4%
0.465 1
2.4%
0.477 1
2.4%
0.479 2
4.8%
0.481 1
2.4%
0.483 1
2.4%
ValueCountFrequency (%)
0.802 1
2.4%
0.785 1
2.4%
0.745 1
2.4%
0.731 2
4.8%
0.718 1
2.4%
0.713 1
2.4%
0.693 1
2.4%
0.662 1
2.4%
0.632 1
2.4%
0.618 1
2.4%
Distinct41
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.821429
Minimum18.5
Maximum92.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size464.0 B
2023-11-17T12:23:49.293534image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum18.5
5-th percentile23.025
Q152.95
median63.5
Q378.475
95-th percentile88.405
Maximum92.2
Range73.7
Interquartile range (IQR)25.525

Descriptive statistics

Standard deviation19.909374
Coefficient of variation (CV)0.31692012
Kurtosis-0.40334292
Mean62.821429
Median Absolute Deviation (MAD)13.7
Skewness-0.64784884
Sum2638.5
Variance396.38319
MonotonicityNot monotonic
2023-11-17T12:23:49.375756image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
76.6 2
 
4.8%
22.6 1
 
2.4%
52.2 1
 
2.4%
83.8 1
 
2.4%
55.6 1
 
2.4%
35.1 1
 
2.4%
79.2 1
 
2.4%
60.1 1
 
2.4%
75.6 1
 
2.4%
58.8 1
 
2.4%
Other values (31) 31
73.8%
ValueCountFrequency (%)
18.5 1
2.4%
21.4 1
2.4%
22.6 1
2.4%
31.1 1
2.4%
31.6 1
2.4%
35.1 1
2.4%
41.2 1
2.4%
41.4 1
2.4%
43.1 1
2.4%
52 1
2.4%
ValueCountFrequency (%)
92.2 1
2.4%
90.3 1
2.4%
88.6 1
2.4%
84.7 1
2.4%
84.6 1
2.4%
84.3 1
2.4%
83.8 1
2.4%
80.9 1
2.4%
79.9 1
2.4%
79.2 1
2.4%

Interactions

2023-11-17T12:23:44.033364image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:27.573255image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:28.744083image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:29.989124image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:30.998651image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:32.312098image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:33.449901image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:34.731480image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:35.859322image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:37.094848image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:38.155787image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:39.411299image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:40.489390image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:41.700986image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:42.792754image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:44.116360image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:27.666255image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:28.821083image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:30.064125image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:31.075649image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:32.390098image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:33.529900image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:34.812479image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:35.932323image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:37.168848image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:38.233785image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:39.488298image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:40.561390image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:41.776986image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:42.876753image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:44.192359image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:27.741254image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:28.888083image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:30.127125image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:31.145652image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:32.458098image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:33.603905image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:34.887479image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:36.000321image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:37.235848image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:38.302787image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:39.557299image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:40.628391image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:41.846985image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:42.944755image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:44.263360image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:27.818260image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:28.951084image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:30.187134image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:31.209650image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:32.522098image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:33.670901image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:34.959479image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:36.060319image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:37.299358image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:38.371787image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:39.621300image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:40.689390image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:41.910985image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:43.008755image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:44.342361image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:27.896950image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:29.022083image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:30.255135image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:31.282648image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:32.594098image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:33.745901image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:35.035479image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:36.130324image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:37.370788image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:38.446787image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:39.696307image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:40.761392image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:41.984502image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:43.080815image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:44.418360image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:27.971478image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:29.092220image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:30.319137image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:31.352655image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:32.669100image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:33.814900image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:35.104480image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:36.196839image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:37.439787image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:38.516788image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:39.769320image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:40.828390image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:42.055015image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:43.150818image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:44.497360image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:28.047478image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:29.164219image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:30.385649image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:31.426650image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:32.752100image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:33.888900image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:35.180485image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:36.268836image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:37.508785image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:38.586787image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:39.841308image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:40.900391image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:42.133014image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:43.401742image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:44.581363image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:28.127495image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:29.240219image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:30.458649image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:31.510650image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:32.841387image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:33.964902image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:35.257489image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:36.343834image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:37.584790image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:38.663787image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:39.915307image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:41.140464image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:42.209014image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:43.480739image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:44.655360image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:28.201494image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:29.312220image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:30.522650image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:31.583651image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:32.915387image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:34.033902image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:35.324489image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:36.412838image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:37.655787image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:38.896301image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:39.985308image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:41.207464image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:42.276022image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:43.545743image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:44.733360image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:28.271492image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:29.384563image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:30.587649image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:31.654649image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:33.007387image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:34.105900image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:35.393495image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:36.667844image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:37.724785image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:38.963298image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:40.054308image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:41.274464image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:42.341502image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:43.611487image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:44.813839image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:28.348495image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:29.456563image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:30.658648image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:31.728650image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:33.083388image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:34.181900image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:35.471499image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:36.742848image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:37.799787image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:39.040302image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:40.126308image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:41.347463image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:42.415500image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:43.689510image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:44.896841image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:28.425495image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:29.527563image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:30.725650image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:31.801649image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:33.155387image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:34.435961image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:35.543323image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:36.812844image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:37.874787image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:39.107299image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:40.197308image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:41.418464image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:42.497502image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:43.758511image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:44.971842image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:28.500495image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:29.598564image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:30.791649image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:32.079648image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:33.224387image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:34.505965image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:35.615319image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:36.881848image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:37.945787image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:39.178299image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:40.267312image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:41.485985image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:42.566755image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:43.824511image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:45.050852image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:28.581083image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:29.847563image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:30.861650image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:32.161650image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:33.296902image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:34.582475image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:35.696323image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:36.951848image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:38.012787image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:39.253303image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:40.342306image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:41.556985image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:42.641754image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:43.895512image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:45.123850image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:28.663086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:29.913124image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:30.925650image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:32.231648image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:33.370900image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:34.652478image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:35.777323image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:37.018847image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:38.079787image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:39.333301image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:40.410308image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:41.623986image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:42.713757image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T12:23:43.958510image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-11-17T12:23:49.461748image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Agricultural Land( %)Co2-Emissions per tonCPIGDPPopulationInfant mortalityMinimum wageUnemployment ratePopulation: Labor force participation (%)temperaturePrecipitation Depth (mm/year)Gini's indexGDP per capitaHuman Development Index (2021)Prevalence of moderate or severe food insecurity in the total population (percent) (2022)ideal temperature?
Agricultural Land( %)1.000-0.0940.086-0.139-0.0600.129-0.355-0.0410.054-0.3710.0630.251-0.128-0.1000.1090.320
Co2-Emissions per ton-0.0941.0000.1340.9240.696-0.3750.1330.171-0.046-0.236-0.4250.1520.3130.415-0.4290.115
CPI0.0860.1341.0000.1620.3140.124-0.2740.1320.110-0.2070.200-0.006-0.251-0.0970.1230.000
GDP-0.1390.9240.1621.0000.840-0.280-0.0500.0380.010-0.199-0.3280.1220.1810.269-0.3150.182
Population-0.0600.6960.3140.8401.0000.040-0.352-0.3000.238-0.070-0.1330.032-0.316-0.143-0.0120.000
Infant mortality0.129-0.3750.124-0.2800.0401.000-0.165-0.4070.3310.2930.3110.022-0.666-0.7720.6890.000
Minimum wage-0.3550.133-0.274-0.050-0.352-0.1651.0000.565-0.5390.084-0.258-0.1420.5400.402-0.3010.166
Unemployment rate-0.0410.1710.1320.038-0.300-0.4070.5651.000-0.585-0.222-0.4900.2150.6050.486-0.4960.252
Population: Labor force participation (%)0.054-0.0460.1100.0100.2380.331-0.539-0.5851.000-0.1740.3950.298-0.433-0.3740.5240.234
temperature-0.371-0.236-0.207-0.199-0.0700.2930.084-0.222-0.1741.0000.001-0.261-0.257-0.3740.1110.861
Precipitation Depth (mm/year)0.063-0.4250.200-0.328-0.1330.311-0.258-0.4900.3950.0011.000-0.086-0.329-0.2770.4770.399
Gini's index0.2510.152-0.0060.1220.0320.022-0.1420.2150.298-0.261-0.0861.0000.1980.1080.2210.000
GDP per capita-0.1280.313-0.2510.181-0.316-0.6660.5400.605-0.433-0.257-0.3290.1981.0000.819-0.6230.135
Human Development Index (2021)-0.1000.415-0.0970.269-0.143-0.7720.4020.486-0.374-0.374-0.2770.1080.8191.000-0.6940.000
Prevalence of moderate or severe food insecurity in the total population (percent) (2022)0.109-0.4290.123-0.315-0.0120.689-0.301-0.4960.5240.1110.4770.221-0.623-0.6941.0000.143
ideal temperature?0.3200.1150.0000.1820.0000.0000.1660.2520.2340.8610.3990.0000.1350.0000.1431.000

Missing values

2023-11-17T12:23:45.249850image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-17T12:23:45.424848image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-11-17T12:23:45.795852image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

CountryCountry CodeAgricultural Land( %)Co2-Emissions per tonCPIGDPPopulationInfant mortalityMinimum wageUnemployment ratePopulation: Labor force participation (%)temperatureideal temperature?Precipitation Depth (mm/year)Gini's indexGDP per capitaHuman Development Index (2021)Prevalence of moderate or severe food insecurity in the total population (percent) (2022)
0AlgeriaNaN0.174150006.0151.361699882363984305305420.10.950.11700.41226.301.089.027.63948.3432790.74522.6
1AngolaNaN0.47534693.0261.73946354158703182529551.60.710.06890.77526.691.01010.051.32973.5911600.58679.9
2BeninNaN0.3336476.0110.71143907090951180115160.50.390.02230.70929.711.01039.037.91219.4326720.52575.5
3BotswanaNaN0.4566340.0149.7518340510789234617930.00.290.18190.70826.061.0416.053.37817.1830830.69360.0
4Burkina FasoNaN0.4423418.0106.58157458102352032137849.00.340.06260.66430.980.0748.043.0774.8396900.44959.8
5Cape VerdeNaN0.196543.0110.50198184574148362816.70.680.12250.605NaNNaN228.042.44097.8722100.66241.2
6CameroonNaN0.2068291.0118.65387604670332587638050.60.350.03380.76126.451.01604.046.61497.9091760.57663.4
7Central African RepublicNaN0.082297.0186.862220307369474518584.50.370.03680.720NaNNaN1343.043.0467.9074410.40484.6
8ComorosNaN0.715202.0103.62118572867785088651.30.710.04340.433NaNNaN900.045.31393.5223720.55884.7
9Republic of the CongoNaN0.3113282.0124.7410820591131538050836.20.880.09470.69427.071.01646.048.92011.0723990.47990.3
CountryCountry CodeAgricultural Land( %)Co2-Emissions per tonCPIGDPPopulationInfant mortalityMinimum wageUnemployment ratePopulation: Labor force participation (%)temperatureideal temperature?Precipitation Depth (mm/year)Gini's indexGDP per capitaHuman Development Index (2021)Prevalence of moderate or severe food insecurity in the total population (percent) (2022)
32Sierra LeoneNaN0.5471093.0234.163941474311781321578.50.570.04430.579NaNNaN2526.035.7504.4625430.47792.2
33South AfricaNaN0.798476644.0158.933514316492415855827028.5NaN0.28180.56019.760.0495.063.06001.4008140.71321.4
34South SudanNaNNaN1727.04583.71119978007511106211363.7NaN0.12240.724NaNNaN900.044.11084.5849030.38588.6
35SudanNaN0.28720000.01344.19189022844764281323842.10.410.16530.48431.360.0250.034.2441.5056030.50852.0
36TanzaniaNaN0.44811973.0187.43631770681755800546337.60.090.01980.83423.491.01071.040.51089.1572090.54960.9
37TogoNaN0.7023000.0113.305459979417808236647.40.340.02040.77629.481.01168.042.5675.5422130.53965.3
38TunisiaNaN0.64829937.0155.33387977099241169471914.60.470.16020.46121.371.0207.032.83317.5410140.73131.6
39UgandaNaN0.7195680.0173.87343872294864426959433.80.010.01840.70326.521.01180.042.7776.7685760.52576.6
40ZambiaNaN0.3215141.0212.31230647224461786103040.40.240.11430.74626.751.01020.055.91291.3433570.56576.0
41ZimbabweNaN0.41910983.0105.51214407588001464546833.9NaN0.04950.83125.371.0657.050.31463.9859100.59377.8